Scoping review of exposure questionnaires and surveys in interstitial lung disease
Bibliographic record
Abstract
BACKGROUND: Many interstitial lung diseases (ILDs) have clear causal relationships with environmental and occupational exposures. Exposure identification can assist with diagnosis, understanding disease pathogenesis, prognostication and prevention of disease progression and occurrence in others at risk. Despite the importance of exposure identification in ILD, there is no standardised assessment approach. Many questionnaires are in clinical and research use, yet their utility, applicability, relevance and performance characteristics are unknown. OBJECTIVES: This scoping review aimed to summarise the available evidence relating to ILD exposure assessment questionnaires, identify research gaps and inform the content for a future single evidence-based ILD questionnaire. METHODS: A scoping review based on Arksey and O'Malley's methodological framework was conducted. ELIGIBILITY CRITERIA: Any questionnaire that elicited exposures specific to ILD was included. A modified COSMIN Risk of Bias Framework was used to assess quality. SOURCES OF EVIDENCE: Relevant articles were identified from MEDLINE and EMBASE up to 23 July 2023. RESULTS: 22 exposure questionnaires were identified, including 15 generally pertaining to ILD, along with several disease-specific questionnaires for hypersensitivity pneumonitis (n=4), chronic beryllium disease, sarcoidosis and silicosis (1 questionnaire each). For most questionnaires, quality was low, whereby the methods used to determine exposure inclusion and questionnaire validation were not reported or not performed. Collectively the questionnaires covered 158 unique exposures and at-risk occupations, most commonly birds, mould/water damage, wood dust, asbestos, farming, automotive mechanic and miners. Only five questionnaires also provided free-text fields, and 13 queried qualifiers such as temporality or respiratory protection. CONCLUSIONS: Designing a robust ILD-specific questionnaire should include an evidence-based and relevance-based approach to exposure derivation, with clinicians and patients involved in its development and tested to ensure relevance and feasibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".